EnhancerNet: a predictive model of cell identity dynamics through enhancer selection
File(s)
Published version
Author(s)
Karin, Omer
Type
Journal Article
Abstract
Understanding how cell identity is encoded by the genome and acquired during differentiation is a central challenge in cell biology. I have developed a theoretical framework called EnhancerNet, which models the regulation of cell identity through the lens of transcription factor-enhancer interactions. I demonstrate that autoregulation in these interactions imposes a constraint on the model, resulting in simplified dynamics that can be parameterized from observed cell identities. Despite its simplicity, EnhancerNet recapitulates a broad range of experimental observations on cell identity dynamics, including enhancer selection, cell fate induction, hierarchical differentiation through multipotent progenitor states and direct reprogramming by transcription factor overexpression. The model makes specific quantitative predictions, reproducing known reprogramming recipes and the complex haematopoietic differentiation hierarchy without fitting unobserved parameters. EnhancerNet provides insights into how new cell types could evolve and highlights the functional importance of distal regulatory elements with dynamic chromatin in multicellular evolution.
Date Issued
2024-10-01
Date Acceptance
2024-09-06
Citation
Development, 2024, 151 (19)
ISSN
0950-1991
Publisher
The Company of Biologists
Journal / Book Title
Development
Volume
151
Issue
19
Copyright Statement
© 2024. Published by The Company of Biologists Ltd This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39289870
PII: 362293
Subjects
Cell fate
Cell identity
Developmental Biology
Dynamical systems
Enhancer selection
FATE
GENES
Life Sciences & Biomedicine
LINEAGE-COMMITMENT
NETWORK MOTIFS
PRECEDES COMMITMENT
PROGENITOR
Science & Technology
SHADOW ENHANCERS
STATES
Statistical physics
SUPER-ENHANCERS
Systems biology
TRANSCRIPTION FACTORS
Publication Status
Published
Coverage Spatial
England
Article Number
dev202997
Date Publish Online
2024-10-09